Evolutionary fuzzy control of two cooperative object-carrying wheeled robots for wall following through multiobjective continuous ACO

Huang, Chen-An; Juang, Chia‐Feng

doi:10.1109/ifsa-scis.2017.8023297

Cited by 3 publications

(2 citation statements)

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“…Ant Colony Optimization (ACO) algorithms attempt to mimic the collective behavior of ants. Huang and Juang [8] have used ACO to develop an algorithm capable of guiding a two-robot team in a wall-following pattern. Castillo et al [2] performed a comparison of ACO and PSO to tune the FLS parameters and Juang et al [9] have implemented a hybrid ACO and PSO algorithm that seeks to use the advantages of each to guide a two-robot team in an object carrying task.…”

Section: Introductionmentioning

confidence: 99%

“…In the previously cited works performing an objectcarrying task [8,9,12,14], the control architecture was based on a leader-follower paradigm, where the leader makes the path planning decisions for the robot team and the follower(s) rely on the leader for direction. In a decentralized architecture, each agent in the robot team is independent and determines its own actions without the need for explicit communication.…”

Section: Introductionmentioning

confidence: 99%

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Genetic fuzzy-based method for training two independent robots to perform a cooperative task

Barth

Sun

Zhang

et al. 2021

Intel Serv Robotics

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When two skilled human workers cooperate on a task, such as moving a sofa through a tight doorway, they often infer what needs to be done without explicit communication because they have learned cooperation skills from their prior work or training. This paper extends that concept to a two-robot team. The robots are given the task to carry a large payload through a narrow doorway while avoiding obstacles within the room. System dynamics and sensor noise were included in the study. Each robot is independently controlled with the knowledge of the goal location, its own position, and the pose of the payload. The decentralized control uses a Genetic Fuzzy System for each robot to learn its own decision-making skill through a training process without a pre-planned motion trajectory. The introduction of a genetic algorithm adds efficiency to the process of determining the shape of the fuzzy logic membership functions by using an evolutionary search algorithm to tune each parameter in the fuzzy system simultaneously. The contribution of this paper is to illustrate how genetic training can tune a simple, decentralized Fuzzy Logic System based on a given scenario and then be used, unaltered, for a scenario beyond that for which it was trained. The extended scenarios introduce unknown obstacles, new sizes and mass properties for the robots and payload, and random initial positions. The effectiveness of this approach for a 2D case is determined by dynamic simulation with results starting at a 95% success rate for the baseline scenario and 84% for the scenario that was extended furthest from how it was originally trained.

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Section: Introductionmentioning

confidence: 99%